AI module monitors and optimises industrial operations

This data-driven analytics algorithm is delivered inside a module that fits directly into the controller chassis. Once installed, Project Sherlock AI leverages novel physics-based modeling to 'learn' the application that controller manages. The solution scours controller tags to identify the application or allows users to choose what they would like modeled by selecting inputs and outputs via an add-on-instruction (AOI). Project Sherlock AI will then quickly learn from the stream of data passing through the controller to build a model. This process can be accomplished in a matter of minutes. Vast quantities of historical data are not required, nor must the data ever leave the automation layer.

Once the model is built, the Project Sherlock solution continuously watches the operation looking for anomalies against its derived, principled understanding. If it spots a problem, it can trigger an alarm on an HMI screen or dashboard. Future iterations will go beyond diagnostics to direct users on how to remedy the issue or to automatically adjust system parameters to fix the problem without human intervention.

“Project Sherlock brings industrial producers amazingly smart analytics in a package that is easy to implement,” said Jonathan Wise, Platform Leader for the Control and Visualisation Business, Rockwell Automation. “As our customers undergo digital transformation – using production data to help improve business outcomes – they can’t wait on expert-driven analytics. Even if there were enough industrial data scientists out there, not every company has the time or funds to employ them. This machine-learning tool creates powerful analytics from your automation infrastructure, painlessly – delivering value moments after it’s dropped in the Logix backplane.”

Project Sherlock diagnostics offer drastically reduced false-positive alarms as compared to other artificial intelligence solutions due to its physics-based modelling and foundation in industrial applications. For example, Project Sherlock AI can tell if a boiler temperature shift is related to a benign change in upstream operations or an abnormality that requires correction.

The initial version of Project Sherlock AI will include ready-to-use templates for boiler, pump and chiller operations, ideal for process or hybrid applications. Users can model additional applications with guided configuration.

Communications with the module are prioritised by the controller, so users can select how much data is sent and intervals of communication. The module does not add to controller CPU-load nor add to network traffic. Project Sherlock AI pilots have been running and producing results for the past 18 months. Customers will be able to purchase the module in mid-2018.

This new artificial intelligence engine is part of a larger, expanding ecosystem of analytics offerings from Rockwell Automation that run across the plant floor for devices, machines and systems, as well as throughout the enterprise. Rockwell Automation developers are building connections so users who employ FactoryTalk Analytics for Devices tools will be able to interface with Project Sherlock AI via the Shelby chatbot and action cards. Analytics from Project Sherlock AI will be easily integrated into the FactoryTalk Analytics Platform to integrate plant-floor data into business intelligence strategies.